Battery Data Scientist Hybrid (San Francisco, CA) | Remote (California, USA)

About Byterat

Byterat

is the modern platform for battery science. We give battery teams at leading electric vehicle, grid storage, electronics and materials companies (e.g. Panasonic, Tesla) the core data platform they need to innovate, securely at scale.

We’re a

well-funded, VC-backed start-up

with customers and recurring revenue. We’re backed by world-class investors like Y Combinator, Giant Ventures and Collaborative Fund, and angels such as founders of Zendesk and Voi, and executives from Google, Meta and Figma.

The Byterat platform operates in battery labs across three continents and our reach is growing rapidly, which presents career-defining opportunities for ambitious engineers to accelerate their growth and contribute to a quickly evolving startup in SF. You’ll be joining our team of engineers and physicists with backgrounds from U. Cambridge, Georgia Tech, U. Waterloo and other YC start-ups.

Our mission

Batteries play a key role in tackling climate change. $73B was invested in US battery plants in 2022, and battery company revenue is expected to grow >

5x in the next decade.

Car manufacturers alone are forecast to spend $515B on electric vehicle and battery R&D by 2030. Yet battery engineers are underserved by software, relying on broken legacy products to analyze their data. Most data is never analyzed or used meaningfully.

Byterat provides battery teams with the data foundation needed to unlock business value from their battery data. Labs use Byterat to analyze data from thousands of parallel experiments and unlock previously hidden insights connecting battery design to performance.

Your mission in this role

This is a one-of-a-kind role for a smart, hard-working, high-agency battery scientist with strong data science and coding skills to wear many hats and play a fundamental role in the development of a world-class product that can shape the outcome of the battery industry.

Take ownership for next-generation battery model development at Byterat for the purposes of predicting anomalies, predicting cycle life.

Take ownership for the expansion of battery science capability within the core Byterat platform.

Work with the Byterat AI team to deliver SOTA battery science features within the platform.

Work with the Byterat Data Engineering team to develop and deploy battery models and metrics within the platform.

Take ownership for working directly with Byterat customers (battery scientists) and translating their feedback into new feature designs.

Take ownership for the integration of new battery data (customers and public data) into the platform.

We might be a fit for you if :

You have a combination of a deep understanding of batteries and data science skills.

You have previously built battery models (physics-based, ECMs, machine learning etc) and have a strong understanding for the differences between them. You have strong data engineering, data science & coding skills.

You are passionate about the adoption of AI for productivity, and you have demonstrated this through side-projects experimenting with LLMs.

You are passionate about the battery industry and our mission.

You have a “founder-like” mentality - you own problems end-to-end and you’ll leverage all your resources to get the job done.

You’re a team player. You have a humble attitude, you take actions to help your colleagues, and you want to do whatever it takes to make the team succeed. You’re reliable on delivering on technical milestones.

You’re intrinsically motivated and set an exceptionally high performance bar for yourself.

You communicate effectively and respectfully. You pay attention to details. You express ideas clearly, you listen with openness. You have the confidence to communicate what is working and what needs to change.

Technical qualifications

You have a BS / MS / PhD in CS, Engineering, Physics, Electrochemistry or a related field.

You have a deep understanding of electrochemical principles, battery degradation mechanisms, and performance modeling.

You have hands-on experience with physics-based and data-driven battery modeling approaches, including parameter estimation and state-of-health prediction.

You have the ability to interpret lab and field data, including voltage, current, impedance, and temperature measurements.

You have hands-on experience with battery modeling techniques, including physics-based and data-driven approaches. Bonus if you have proficiency using open-source battery modelling libraries.

You have experience in writing clean, efficient, and production-ready Python code.

You have experience developing, training, and deploying machine learning models.

You have experience preprocessing, cleaning, and integrating large-scale battery datasets, both structured and unstructured.

About the interview

Intro call with our founder

Live Python coding challenge

Technical deep-dive

On a relevant battery data problem you’ve personally solved

On an example problem you might be working on within our team

Career deep-dive

Get to know everyone on our team

Reference checks

J-18808-Ljbffr